Data classification is a technique for estimating a class to which given unclassified data belongs, which is one of most basic components in data analysis. In particular, a data classification technique using a separation surface which divides a feature space into a plurality of regions, such as a separation surface between classes has high capability of representing a model. For this reason, the technique can be applied to wide range of problems and data structures including data classification of image data, protein and gene data, and even can be applied to: failure diagnosis in a case where the class label is set to be information about failure; and link presumption in a case where the class label is set to be the presence/absence of link between networks such as the Internet or social networks.
The data classification method using a separation surface is broadly categorized into two techniques, namely, the identification and the outlying value classification. In the former one, by learning a separation surface for separating classes based on data having a class label, classification target data is classified into known classes. In the latter one, by regarding training data as one class and learning a separation surface for separating a region where the training data is distributed from the other region, it is judged that the classification target data belongs to the class or not. As the data classification method capable of simultaneously performing identification and outlying value classification, some combinations of data classification methods using the separation surface can be easily inferred.
First, in a case where the number of the class regarding to the training data is one, data classification is outlying value classification. Thus, use of a known outlying value classification technique such as 1 class support vector machine (Chapter 8 of Document 5, and Document 3) is considered.
Next, in a case where the number of classes regarding to the training data is two or more, the following method can be adopted: outlying value classification such as the 1 class support vector machine is independently learnt for each class. When classification target data is determined as an outlying value to all classes, the data is an outlying value and when the classification target data is determined to belong to one or a plurality of classes, the data is classified into the one or the plurality of classes.
In the case where the number of the class regarding to the training data is two or more, another method can be adopted. An outlying value classification method such as the 1 class support vector machine is combined with a identification method using a separation surface such as the support vector machine (Document 1, Document 2, and Document 6). First, all of classes are learnt by the outlying value classification method and then, identification is made for known classes. According to this method, it is determined whether classification target data is an outlying value by an outlying value detection method, and when the data is not an outlying value, it is determined which of known classes the data belongs to by the identification method.
On the other hand, as a technique using a plurality of separation surfaces, the multi-class support vector machine is exemplified. As the method for implementing the multi-class support vector machine, there are a method of calculating 2 class support vector machine for each combination of classes and taking a majority vote and a method of simultaneously optimizing a plurality of hyperspaces such as methods proposed in Document 7 and Document 4.
Related documents are listed below.    Document 1: Japanese Unexamined Patent Publication No. 2007-115245    Document 2: Japanese Unexamined Patent Publication No. 2007-95069    Document 3: Japanese Unexamined Patent Publication No. 2005-345154    Document 4: Japanese Unexamined Patent Publication No. 2007-52507    Document 5: Bernhard Scholkopf and Alex Smola. Learning with Kernels, Support Vector Machines, Regularization, Optimization and Beyond. MIT Press. 2002.    Document 6: Bernhard Scholkopf, Alex J. Smola, Robert C. Williamson and Peter L. Bartlett. New Support Vector Algorithms. Neural Computation. Vol. 12: page 1207-1245, 2000.    Document 7: Ioannis Tsochantaridis, Thorsten, Joachims, Thoms Hofmann, Yasemin Altun. Large Margin Methods for Structured and Interdependent Output Variables, Journal of Machine Learning Research Vol 6: page 1453-1484, 2005.    Document 8: A. L. Yuille and A. Rangarajan. The concave-convex procedure. Neural Computation. Vol 15: page 915-936, 2003.